Wetland natural reserve migrant bird real-time distribution mapping method and system

Abstract

The invention relates to the technical field of biodiversity change monitoring informatization, in particular to a real-time distribution mapping method and system for migrant birds in a wetland natural reserve. Comprising the following steps: arranging a plurality of video monitoring cameras to obtain a plurality of video data; extracting a corresponding video frame according to each piece of video data, and identifying the number of migrant birds to obtain migrant bird identification data; assigning a value to each monitoring coverage area according to the migrant bird identification data to obtain an assignment result; and performing color filling on each monitoring coverage area, refreshing the color filling result, and performing superposition display to obtain a migrant bird real-time distribution thermodynamic diagram. The space distribution condition of the migrant birds is obtained in real time, statistical calculation is performed based on the actual space distribution condition, the change condition of the number of the birds is obtained and displayed, the problem that space-time distribution of migrant bird monitoring is difficult is solved, all data are archived in the background, the effect that monitoring data can be traced back and checked is achieved, and the monitoring efficiency is improved. The cost is controllable, and the efficiency is high.

Description

Technical Field

[0001] The present invention relates to the field of biodiversity change monitoring information technology, and in particular to a real-time distribution mapping method for migratory birds in a wetland nature reserve and a system thereof. Background Art

[0002] At present, the observation of migratory birds in nature reserves is mainly carried out through on-site surveys by experts, and relies on personal experience to solve the problems of where the migratory birds in wetland reserves are and how they are distributed spatially. This is costly and inefficient. However, on-site surveys can only rely on a large number of people with experience in identifying migratory birds, and the data is difficult to trace back. On-site surveys may also affect the distribution of birds and have large errors. Summary of the invention

[0003] The present invention aims to at least improve one of the technical problems existing in the prior art. To this end, the present invention proposes a real-time distribution mapping method and system for migratory birds in a wetland nature reserve.

[0004] A real-time distribution mapping method for migratory birds in a wetland nature reserve according to an embodiment of the first aspect of the present invention is applied to the distribution monitoring of migratory birds in a wetland nature reserve, wherein a plurality of video surveillance cameras are deployed in the wetland nature reserve, and the method comprises:

[0005] Obtaining the monitoring coverage of each video surveillance camera and a plurality of video data corresponding to the monitoring coverage;

[0006] Extracting corresponding video frames according to each video data, performing enhancement processing on the video frames, and identifying the number of migratory birds in each video frame;

[0007] According to the number of migratory birds in each video frame, the total number of migratory birds in each monitoring coverage area is calculated to obtain migratory bird identification data;

[0008] Assign a value to each monitoring coverage area according to the migratory bird identification data to obtain the assignment result of each monitoring coverage area;

[0009] Each monitoring coverage area is filled with color according to the assignment result, and the color filling result is refreshed and superimposed to obtain a real-time distribution heat map of migratory birds.

[0010] In a possible implementation manner of the first aspect, extracting a corresponding video frame for each video data specifically includes the following contents:

[0011] Resize the input image in the video data until it meets the unified target size requirement;

[0012] Divide the adjusted input image into grid cells;

[0013] Feature extraction: Use a convolutional neural network (CNN) to extract features from each grid of the input image to predict bounding boxes and class probabilities;

[0014] Predict multiple bounding boxes for each grid cell. Each bounding box consists of five components: the x-coordinate, y-coordinate, width w, height h, and confidence score of the center point of the target object. The bounding box prediction result is obtained.

[0015] Perform category prediction on each grid cell and obtain the category probability C, which is used to represent the probability of the existence of migratory birds in the grid cell;

[0016] A combined prediction is performed based on the bounding box prediction results and the category probability C to generate a list of detected objects.

[0017] Perform non-maximum suppression according to the detection object list, and remove and filter out redundant bounding boxes in the detection object list whose confidence scores are lower than a target threshold;

[0018] Output the video frames corresponding to the detected object list.

[0019] In a possible implementation manner of the first aspect, the color filling of each monitoring coverage area is specifically performed by gradient filling from dark to light according to the numerical value of the assignment result.

[0020] In a possible implementation of the first aspect, the acquisition of the monitoring coverage of each video surveillance camera and the multiple video data corresponding to the monitoring coverage is that in each working cycle, the video surveillance camera performs a 360° rotation scan and records video every two hours.

[0021] In a possible implementation of the first aspect, the enhanced processing of the video frames includes converting low-resolution video frames into high-resolution video frames using algorithm interpolation or a machine learning model, thereby improving the clarity of the video.

[0022] In a possible implementation manner of the first aspect, the enhancement processing of the video frame also includes noise reduction processing to reduce noise and graininess in the video frame and improve the smoothness and clarity of the video.

[0023] According to the real-time distribution mapping method of migratory birds in wetland nature reserves according to an embodiment of the present invention, the spatial distribution status of migratory birds is obtained in real time by a video surveillance system deployed in the wetland nature reserve, and statistical calculations are performed based on the actual spatial distribution status to obtain and display the change in the number of birds around the video surveillance camera, thereby solving the problem of the difficulty in monitoring the temporal and spatial distribution of migratory birds, and all data are archived in the background, achieving the effect of traceability and verifiability of monitoring data, with controllable costs and high efficiency.

[0024] A real-time distribution mapping system for migratory birds in a wetland nature reserve according to an embodiment of the second aspect of the present invention, which is used to implement the above method, includes:

[0025] A plurality of video surveillance cameras are arranged in the wetland nature reserve, and are used to obtain the monitoring coverage of each video surveillance camera and a plurality of video data corresponding to the monitoring coverage;

[0026] A processing module, used for extracting corresponding video frames according to each video data, performing enhancement processing on the video frames, and identifying the number of migratory birds in each video frame;

[0027] A calculation module, used to calculate the total number of migratory birds in each monitoring coverage area according to the number of migratory birds in each video frame, and obtain migratory bird identification data;

[0028] An assignment module is used to assign a value to each monitoring coverage area according to the migratory bird identification data to obtain an assignment result for each monitoring coverage area;

[0029] The mapping module is used to fill each monitoring coverage area with color according to the assignment result, refresh the color filling result, and overlay and display it to obtain a real-time distribution heat map of migratory birds.

[0030] In a possible implementation manner of the second aspect, in the mapping module, the color filling of each monitoring coverage area is specifically performed by gradient filling from dark to light according to the numerical value of the assignment result.

[0031] A computer device according to an embodiment of the third aspect of the present invention includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned method for mapping the real-time distribution of migratory birds in a wetland nature reserve when executing the computer program.

[0032] According to a computer storage medium of an embodiment of the fourth aspect of the present invention, instructions are stored in the computer storage medium, and when the instructions are executed on a computer, the computer executes the real-time distribution mapping method of migratory birds in a wetland nature reserve as described above.

[0033] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 is a flow chart of a method for mapping the real-time distribution of migratory birds in a wetland nature reserve according to an embodiment of the present invention;

[0036] Figure 2 This is a real-time distribution heat map of migratory birds in a wetland nature reserve according to an embodiment of the present invention.

[0037] Figure 3 1 is a coverage map of migratory bird video surveillance cameras in a wetland nature reserve according to an embodiment of the present invention.

[0038] Figure 4 It is a video frame shot by a video surveillance camera for migratory birds in a wetland nature reserve according to an embodiment of the present invention.

[0039] Figure 5 This is a grid unit division picture after the size of the input image of the migratory bird video in the wetland nature reserve is adjusted according to an embodiment of the present invention.

[0040] Figure 6 It is a schematic diagram of the prediction process consisting of the prediction box and species confidence after analyzing the video image of migratory birds in a wetland nature reserve according to an embodiment of the present invention.

[0041] Figure 7 This is a picture of the migratory bird video recognition effect in a wetland nature reserve according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The embodiments of the present invention are described in detail below. The embodiments described with reference to the accompanying drawings are exemplary. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0043] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element at the same time.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0045] The terms "first", "second", "third", etc. in the specification and claims of the present application and the drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a series of steps or units are included, or optionally, steps or units not listed are included, or optionally, other steps or units inherent to these processes, methods, products or devices are included.

[0046] Only the part relevant to the present application but not all content is shown in the accompanying drawings.Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processing or methods described as flow charts.Although flow charts describe various operations (or steps) as sequential processing, many operations therein can be implemented in parallel, concurrently or simultaneously.In addition, the order of various operations can be rearranged.Described processing can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings.Described processing can correspond to method, function, procedure, subroutine, subprogram etc.

[0047] As used in this specification, the terms "component", "module", "system", "unit", etc. are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a unit can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or distributed between two or more computers. In addition, these units can be executed from various computer-readable media having various data structures stored thereon. Units can communicate through local and / or remote processes, for example, based on signals having one or more data packets (e.g., data from a second unit interacting with another unit in a local system, a distributed system, and / or a network. For example, the Internet interacts with other systems via signals).

[0048] Example 1

[0049] See also Figures 1 to 7 As shown, this embodiment provides a real-time distribution mapping method for migratory birds in a wetland nature reserve, which is applied to the distribution monitoring of migratory birds in a wetland nature reserve, wherein a plurality of video surveillance cameras are deployed in the wetland nature reserve, and the method comprises:

[0050] S1, obtaining the monitoring coverage of each video surveillance camera and a plurality of video data corresponding to the monitoring coverage;

[0051] S2, extracting corresponding video frames according to each video data, performing enhancement processing on the video frames, and identifying the number of migratory birds in each video frame;

[0052] S3, calculating the total number of migratory birds in each monitoring coverage area according to the number of migratory birds in each video frame, and obtaining migratory bird identification data;

[0053] S4, assigning a value to each monitoring coverage area according to the migratory bird identification data to obtain an assignment result for each monitoring coverage area;

[0054] S5, fill each monitoring coverage area with color according to the assignment result, refresh the color filling result, and superimpose and display it to obtain a real-time distribution heat map of migratory birds, such as Figure 3 As shown, the numerical value within each monitoring coverage area represents the number of identified birds.

[0055] It should be noted that extracting the corresponding video frame for each video data specifically includes the following contents:

[0056] Select a video of a wetland national nature reserve in Poyang Lake, and convert the input image in the video data (such as Figure 4 The size is adjusted until the uniform target size requirement is met;

[0057] It should be noted that the input image in the video data is resized to meet 448×448 pixels to ensure a consistent processing process.

[0058] Divide the adjusted input image into grid cells: Divide the image into a 32x32 grid. Each grid cell is responsible for detecting the target whose center point falls within the cell (such as Figure 5 shown);

[0059] Feature extraction: Use a convolutional neural network (CNN) to extract features from each grid of the input image to predict bounding boxes and class probabilities;

[0060] Multiple bounding boxes are predicted for each grid unit. Each bounding box contains five components, namely the x-coordinate, y-coordinate, width w, height h and confidence score of the center point of the target object. The bounding box prediction result (such as Figure 6 As shown), in this embodiment, the target object is a migratory bird, and the parameters of the corresponding five predicted bounding boxes are as follows:

[0061] ① Bounding box 1: center point pixel coordinates, x(205), y(18), width(20), height(49), confidence score(54%);

[0062] ② Bounding box 2: center point pixel coordinates, x (212), y (226), width (48), height (60), confidence score (46%);

[0063] ③ Bounding box 3: center point pixel coordinates, x (204), y (245), width (48), height (74), confidence score (39%);

[0064] ④ Bounding box 4: center point pixel coordinates, x (224), y (259), width (32), height (50), confidence score (24%);

[0065] ⑤ Bounding box 5: center point pixel coordinates, x (217), y (279), width (27), height (23), confidence score (33%);

[0066] Perform category prediction on each grid cell and obtain the category probability C, which is used to represent the probability of the existence of migratory birds in the grid cell;

[0067] A combined prediction is performed based on the bounding box prediction results and the category probability C to generate a list of detected objects.

[0068] Perform non-maximum suppression according to the detection object list, and remove and filter out redundant bounding boxes in the detection object list whose confidence scores are lower than a target threshold;

[0069] Output the video frame corresponding to the detected object list (such as Figure 7 shown).

[0070] In this embodiment, taking an input image of 416×416 pixels as an example, the total number of predicted bounding boxes is ((52×52)+(26×26)+(13×13))×3=10647 bounding boxes. However, there may be only one target in the actual image. Through confidence threshold filtering and non-maximum suppression, the detection results can be reduced from 10647 to 1.

[0071] It should be noted that the color filling of each monitoring coverage area is specifically performed by gradient filling from dark to light according to the numerical value of the assignment result.

[0072] It should be noted that the acquisition of the monitoring coverage of each video surveillance camera and the multiple video data corresponding to the monitoring coverage is specifically that in each working cycle, the video surveillance camera performs a 360° rotation scan and records video every two hours.

[0073] In this embodiment, each working period is from 7:00 am to 5:00 pm every day.

[0074] It should be noted that the enhanced processing of the video frames includes using algorithm interpolation or machine learning models to convert low-resolution video frames into high-resolution video frames, thereby improving the clarity of the video.

[0075] It should be noted that the enhancement processing of the video frame also includes noise reduction processing to reduce noise and graininess in the video frame and improve the smoothness and clarity of the video.

[0076] According to the real-time distribution mapping method of migratory birds in wetland nature reserves according to an embodiment of the present invention, the spatial distribution status of migratory birds is obtained in real time by a video surveillance system deployed in the wetland nature reserve, and statistical calculations are performed based on the actual spatial distribution status to obtain and display the change in the number of birds around the video surveillance camera, thereby solving the problem of the difficulty in monitoring the temporal and spatial distribution of migratory birds, and all data are archived in the background, achieving the effect of traceability and verifiability of monitoring data, with controllable costs and high efficiency.

[0077] Example 2

[0078] This embodiment provides a real-time distribution mapping system for migratory birds in a wetland nature reserve, which is used to implement the above method, including:

[0079] A plurality of video surveillance cameras are arranged in the wetland nature reserve, and are used to obtain the monitoring coverage of each video surveillance camera and a plurality of video data corresponding to the monitoring coverage;

[0080] A processing module, used for extracting corresponding video frames according to each video data, performing enhancement processing on the video frames, and identifying the number of migratory birds in each video frame;

[0081] A calculation module, used to calculate the total number of migratory birds in each monitoring coverage area according to the number of migratory birds in each video frame, and obtain migratory bird identification data;

[0082] An assignment module is used to assign a value to each monitoring coverage area according to the migratory bird identification data to obtain an assignment result for each monitoring coverage area;

[0083] The mapping module is used to fill each monitoring coverage area with color according to the assignment result, refresh the color filling result, and overlay and display it to obtain a real-time distribution heat map of migratory birds.

[0084] It should be noted that, in the mapping module, the color filling of each monitoring coverage area is specifically performed by gradient filling from dark to light according to the numerical value of the assignment result.

[0085] Example 3

[0086] This embodiment provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the above-mentioned method for mapping the real-time distribution of migratory birds in a wetland nature reserve.

[0087] Example 4

[0088] This embodiment provides a computer storage medium, wherein the computer storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the above-mentioned method for mapping the real-time distribution of migratory birds in a wetland nature reserve.

[0089] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “clockwise”, “counterclockwise”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation to the invention.

[0090] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example.

[0091] Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Mentioning "embodiment" in this article means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present embodiment application. The appearance of this phrase in various positions in the specification is not necessarily the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It can be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0092] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A real-time distribution mapping method for migratory birds in wetland nature reserves, applied to migratory bird distribution monitoring in wetland nature reserves, characterized in that: A plurality of video surveillance cameras are arranged in the wetland nature reserve, and the method comprises: Obtaining the monitoring coverage of each video surveillance camera and a plurality of video data corresponding to the monitoring coverage; Extracting corresponding video frames according to each video data, performing enhancement processing on the video frames, and identifying the number of migratory birds in each video frame; According to the number of migratory birds in each video frame, the total number of migratory birds in each monitoring coverage area is calculated to obtain migratory bird identification data; Assign a value to each monitoring coverage area according to the migratory bird identification data to obtain the assignment result of each monitoring coverage area; Each monitoring coverage area is filled with color according to the assignment result, and the color filling result is refreshed and superimposed to obtain a real-time distribution heat map of migratory birds.

2. The real-time distribution mapping method for migratory birds in wetland nature reserves according to claim 1, characterized in that: Extract the corresponding video frame for each video data, including the following contents: Resize the input image in the video data until it meets the unified target size requirement; Divide the adjusted input image into grid cells; Feature extraction: Use convolutional neural networks to extract features from each grid of the input image; Predict multiple bounding boxes for each grid cell. Each bounding box consists of five components: the x-coordinate, y-coordinate, width w, height h, and confidence score of the center point of the target object. The bounding box prediction result is obtained. Perform category prediction on each grid unit and obtain category probability C; Perform a combined prediction based on the bounding box prediction results and the category probability C to generate a list of detected objects; Perform non-maximum suppression according to the detection object list, and remove and filter out redundant bounding boxes in the detection object list whose confidence scores are lower than a target threshold; Output the video frames corresponding to the detected object list.

3. The real-time distribution mapping method of migratory birds in wetland nature reserves according to claim 1, characterized in that: The color filling of each monitoring coverage area is specifically performed by gradient filling from dark to light according to the numerical value of the assignment result.

4. The real-time distribution mapping method for migratory birds in wetland nature reserves according to claim 1, characterized in that: The obtaining of the monitoring coverage of each video surveillance camera and the multiple video data corresponding to the monitoring coverage is specifically that in each working cycle, the video surveillance camera performs a 360° rotation scan and records video every two hours.

5. The real-time distribution mapping method of migratory birds in wetland nature reserves according to claim 1, characterized in that: The enhanced processing of the video frames includes converting low-resolution video frames into high-resolution video frames using algorithm interpolation or a machine learning model.

6. The real-time distribution mapping method of migratory birds in wetland nature reserves according to claim 5 is characterized in that: The enhancement processing of the video frame also includes noise reduction processing.

7. A real-time distribution mapping system for migratory birds in wetland nature reserves, characterized in that: Used to implement the method according to any one of claims 1 to 6, comprising: A plurality of video surveillance cameras are arranged in the wetland nature reserve, and are used to obtain the monitoring coverage of each video surveillance camera and a plurality of video data corresponding to the monitoring coverage; A processing module, used for extracting corresponding video frames according to each video data, performing enhancement processing on the video frames, and identifying the number of migratory birds in each video frame; A calculation module, used to calculate the total number of migratory birds in each monitoring coverage area according to the number of migratory birds in each video frame, and obtain migratory bird identification data; An assignment module is used to assign a value to each monitoring coverage area according to the migratory bird identification data to obtain an assignment result for each monitoring coverage area; The mapping module is used to fill each monitoring coverage area with color according to the assignment result, refresh the color filling result, and overlay and display it to obtain a real-time distribution heat map of migratory birds.

8. The real-time distribution mapping system for migratory birds in wetland nature reserves according to claim 7 is characterized in that: In the mapping module, the color filling of each monitoring coverage area is specifically performed by gradient filling from dark to light according to the numerical value of the assignment result.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the real-time distribution mapping method for migratory birds in a wetland nature reserve as claimed in any one of claims 1 to 6 when executing the computer program.

10. A computer storage medium, characterized in that: The computer storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the method for mapping the real-time distribution of migratory birds in a wetland nature reserve according to any one of claims 1 to 6.

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